Hardware Demonstration of a Scalable Cognitive Sparse Array

High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection...

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Main Authors: Muleti, Satish, Shavit, Yariv, Namer, Moshe, Eldar, Yonina C.
Format: Conference Proceeding
Language:English
Subjects:
Antenna arrays
Arrays
Complexity theory
Direction-of-arrival estimation
Estimation
Hardware
Receiving antennas
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Shavit, Yariv
Namer, Moshe
Eldar, Yonina C.
description High-resolution direction of arrival estimation requires a large number of antenna elements which increases the computational cost, hardware complexity, and power requirements. To balance between hardware complexity and resolution, recently, we proposed a cognitive, scalable, sparse array selection technique based on a submodular-greedy algorithm. In this demo, we present a design and implementation of a hardware prototype that demonstrate the proposed sparse antenna selection strategy. Through real-time experiments, we show that the proposed sparse selection method results in a 2 − 3 dB lower error compared to a typically employed random selection method.
doi_str_mv 10.1109/RadarConf2043947.2020.9266620
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subjects Antenna arrays
Arrays
Complexity theory
Direction-of-arrival estimation
Estimation
Hardware
Receiving antennas
title Hardware Demonstration of a Scalable Cognitive Sparse Array
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